code: def test_classification(
      classification_data,
      continuous_cols,
      categorical_cols,
      continuous_feature_transform,
      normalize_continuous_features,
  ):
      (train, test, target) = classification_data
      if len(continuous_cols) + len(categorical_cols) == 0:
          return
      data_config = DataConfig(
          target=target,
          continuous_cols=continuous_cols,
          categorical_cols=categorical_cols,
          continuous_feature_transform=continuous_feature_transform,
          normalize_continuous_features=normalize_continuous_features,
      )
      model_config_params = {"task": "classification", "depth": 2}
      model_config = NodeConfig(**model_config_params)
      trainer_config = TrainerConfig(
          max_epochs=1, checkpoints=None, early_stopping=None
      )
      optimizer_config = OptimizerConfig()
  
      tabular_model = TabularModel(
          data_config=data_config,
          model_config=model_config,
          optimizer_config=optimizer_config,
          trainer_config=trainer_config,
      )
      tabular_model.fit(train=train)
  
      result = tabular_model.evaluate(test)
      assert result[0]["valid_loss"] < 2.5
      pred_df = tabular_model.predict(test)
      assert pred_df.shape[0] == test.shape[0]
  
file_path: pytorch_tabular/examples/__only_for_dev__/to_test_node.py
imports: import numpy
  import pandas
  from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig
  from pytorch_tabular.models.node import NodeConfig
  from pytorch_tabular.tabular_model import TabularModel
  from sklearn.datasets import fetch_california_housing, fetch_covtype
label_desc: Use image node_id 4 for calling a global function with example usage: test_classification(classification_data, continuous_cols, categorical_cols, continuous_feature_transform, normalize_continuous_features) without return types